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Development of a microcontroller-based adaptive fuzzy controller for a two-wheeled self-balancing robot

机译:基于微控制器的自适应模糊控制器的开发,用于双轮自平衡机器人

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摘要

In this paper, an intelligent system use an adaptation fuzzy controller using Mamdani algorithm modified by relation models for a two wheeled self balancing robot is developed. Hardware model of the robot and sensor signal processing are described. The signals from sensors are filtered by a discrete complementary filter. A mathematical model of the robot is derived based on Newtonian mechanics. The proposed control system comprises two loops for regulation of the pitch angle and tracking the desired position of the robot. The inner loop uses a PD controller for position tracking. The outer loop is designed with an adaptive fuzzy controller to regulate balancing of the robot. The proposed controllers are tested in simulations using the mathematical model. These controllers are also designed and implemented in the real time system using a STM32F4 DISCOVERY kit which is equipped with a 32-bit ARM7 microprocessor. Simulations and experimental results show advantages of the adaptation fuzzy controller. Using the adaptive fuzzy controller for stability of the robot will allow a more effective and robust control to be implemented.
机译:在本文中,开发了一种智能系统使用使用Mamdani算法的适配模糊控制器,由两个轮式自平衡机器人的关系模型修改。描述了机器人和传感器信号处理的硬件模型。来自传感器的信号通过离散互补滤波器过滤。基于牛顿力学导出机器人的数学模型。所提出的控制系统包括两个环,用于调节桨距角并跟踪机器人的所需位置。内环使用PD控制器进行位置跟踪。外环设计有自适应模糊控制器来调节机器人的平衡。使用数学模型在模拟中测试所提出的控制器。这些控制器还使用配备32位ARM7微处理器的STM32F4 Discovery Kit在实时系统中设计和实现。模拟和实验结果表明,适配模糊控制器的优势。使用自适应模糊控制器以实现机器人的稳定性将允许实现更有效和更强大的控制。

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